consistency by simulation. You choose a set of "true parameters"

the data increases.

> Alexios has given a computational reason

> for needing more data, but there is an

> economic reason as well -- 30 months is

> not enough data to estimate a garch model.

>

> For daily data I regard 1000 observations

> as the absolute minimum to get any sort of

> reasonable estimate.

>

> I think it would be better to avoid the

> estimation step. Here's what I would do

> in this situation:

>

> 1. Get a "standard" set of parameters for

> the garch model. I'm not sure what those

> would be for monthly data. (You can think

> of this as a Bayesian estimate with a very

> narrow prior.)

>

> 2. Given the fixed parameters and the

> variance of the known data, solve for the

> intercept.

>

> 3. Do the prediction with these parameters.

> It is just a bit of arithmetic.

>

> On 23/11/2011 09:33, alexios wrote:

>> As far as rugarch is concerned, the restriction is there for a reason:

>> It is highly unlikely that the solver will converge with anything less

>> than 100 points, and even then, what inference you expect to make with

>> so little data, let alone confidence to perform a forecast is beyond me

>> (the ugarchdistribution function which simulates and fits GARCH models

>> given a parameter set, for different window sizes, can be used to better

>> understand this point).

>> Having said that, the software is open source...open it up, see the

>> source and make the changes you want (hint: the 15th line of code in the

>> file 'rugarch-egarch.R' can be commented out to remove the restriction).

>>

>> Regards,

>> Alexios

>>

>>

>> On 23/11/2011 07:16, hemsam wrote:

>>> Hi,

>>>

>>> Problem : Need to predict the subsequent month vol using the past 30

>>> month

>>> observations

>>>

>>> Tried the rugarch package but there is a limitation which says that

>>> you need

>>> to have atleast 100 observations

>>>

>>> In the fGarch package, one has to use OX interface which does not come

>>> free

>>>

>>> In the egarch package, one can fit an egarch model with less than 100

>>> data

>>> points but then there is no predict function which helps in

>>> forecasting the

>>> one-step ahead forecast

>>>

>>> Appreciate your help and guidance in coming up with a solution for the

>>> problem

>>>

>>> Regards

>>>

>>> --

>>> View this message in context:

>>>

http://r.789695.n4.nabble.com/help-with-egarch-prediction-tp4098716p4098716.html>>>

>>>

>>> Sent from the Rmetrics mailing list archive at Nabble.com.

>>>

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